Solder joint image adaptive block compressive sensing with convex optimisation and Gini index

Abstract

This study aims to improve the performance in solder joint image compression and reconstruction. A novel adaptive block compressive sensing with convex optimisation and Gini index (Ad_BCSGB_Gini) methodology for solder joint image compression and reconstruction is proposed. At first, the image is split into square blocks and each block is resized into a row which consists of a new image. Then, the new image is transformed into a sparse signal by an orthogonal basis matrix, and the image reconstruction is handled as a convex optimisation problem. Moreover, a gradient-based method which has fast computational speed is used to reconstruct image. There is a control factor which controls a norm l(1) in the optimisation problem. To achieve the best performance, at last, the proposed method adaptively selects the best result by comparing Gini index of the reconstruction results based on different control factor values. Experimental results with different methods indicate that the Ad_BCSGB_Gini method is able to achieve the best performance in quantisation comparison than several classical algorithms, and Ad_BCSGB_Gini has a good robustness.

Keywords

• matrix algebra
• iterative methods
• data compression
• optimisation
• gradient methods
• surface mount technology
• image reconstruction
• compressed sensing
• solders
• image coding
• mechanical engineering computing
• ad_bcsgb_gini method
• solder joint image reconstruction performance
• comparing gini index
• convex optimisation problem
• square blocks
• novel adaptive block compressive sensing
• solder joint image compression
• surface mount technology components
• solder joint image adaptive block compressive sensing